Objective. Schizophrenia (SCZ) is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization. Several studies encompass machine learning (ML) and deep learning algorithms to automate the diagnosis of this mental disorder. Others study SCZ brain networks to get new insights into the dynamics of information processing in individuals suffering from the condition. In this paper, we offer a rigorous approach with ML and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in SCZ individuals. Approach. For this purpose, we employed an functional magnetic resonance imaging (fMRI) and electroencephalogram (EEG) dataset. In addition, we combined EEG measures, i.e. Hjorth mobility and complexity, with complex network measurements to be analyzed in our model for the first time in the literature. Main results. When comparing the SCZ group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. Regarding complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most crucial measure in different data modalities. Furthermore, the SCZ brain networks exhibit less segregation and a lower distribution of information. As a result, EEG measures outperformed complex networks in capturing the brain alterations associated with SCZ. Significance. Our model achieved an area under receiver operating characteristic curve (AUC) of 100% and an accuracy of 98.5% for the fMRI, an AUC of 95%, and an accuracy of 95.4% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures on these results, which helped us better describe changes in the diseased brain.

This study reports the virome investigation of pollinator species and other floral visitors associated with plants from the south of Bahia: Aphis aurantii, Atrichopogon sp., Dasyhelea sp., Forcipomyia taiwana, and Trigona ventralis hoozana. Studying viruses in insects associated with economically important crops is vital to understand transmission dynamics and manage viral diseases that pose as threats for global food security. Using literature mining and public RNA next-generation sequencing data deposited in the NCBI SRA database, we identified potential vectors associated with Malvaceae plant species and characterized the microbial communities resident in these insects. Bacteria and Eukarya dominated the metagenomic analyses of all taxon groups. We also found sequences showing similarity to elements from several viral families, including Bunyavirales, Chuviridae, Iflaviridae, Narnaviridae, Orthomyxoviridae, Rhabdoviridae, Totiviridae, and Xinmoviridae. Phylogenetic analyses indicated the existence of at least 16 new viruses distributed among A. aurantii (3), Atrichopogon sp. (4), Dasyhelea sp. (3), and F. taiwana (6). No novel viruses were found for T. ventralis hoozana. For F. taiwana, the available libraries also allowed us to suggest possible vertical transmission, while for A. aurantii we followed the infection profile along the insect development. Our results highlight the importance of studying the virome of insect species associated with crop pollination, as they may play a crucial role in the transmission of viruses to economically important plants, such as those of the genus Theobroma, or they will reduce the pollination process. This information may be valuable in developing strategies to mitigate the spread of viruses and protect the global industry.

There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine (SVM). We found that both approaches could detect changes in the brain’s automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers’ emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.

Cocoa (Theobroma cacao L.) is a major agricultural commodity, essential for the global chocolate industry and the livelihoods of millions of farmers. However, viral diseases pose a significant threat to cocoa production, with Badnavirus species causing severe losses in Africa. Despite its economic importance, the overall virome of T. cacao remains poorly characterized, limiting our understanding of viral diversity and potential disease interactions. This study aims to assess the cocoa-associated virome by analyzing 109 publicly available RNA-seq libraries from nine BioProjects, covering diverse conditions and geographic regions. We implemented a comprehensive bioinformatics pipeline integrating multiple viral sequence enrichment steps, a hybrid assembly strategy using different assemblers, and sequence similarity searches against NCBI non-redundant databases. Our approach identified ten putative novel viruses associated with the cocoa microbiome and a novel Badnavirus species. These findings provide new insights into the viral landscape of T. cacao, characterizing the diversity of cacao-associated viruses and their potential ecological roles. Expanding the catalog of viruses associated with cocoa plants not only enhances our understanding of plant–virus–microbiome interactions but also contributes to the development of more effective disease surveillance and management strategies, ultimately supporting sustainable cocoa production.

The Lagoa Real Uraniferous Province, located between Caetité, Lagoa Real, and Livramento de Nossa Senhora (BA), constitutes the largest uranium reserve in Brazil, with approximately 112,000 tons of U3O8, and an average annual production of 300 tons. Its Archean and Paleoproterozoic rocks, rich in mineralized albitites, constitute an important natural source of radiation, generating geochemical anomalies that are potentially harmful to the environment and human health. This study aimed to correlate the geochemical behavior of uranium (U), lead-210 (210Pb), and radium (226Ra/228Ra) in soils in the region, aiming to understand their distribution, interrelationships, and geoenvironmental implications. Twenty samples were collected and analyzed at the Environmental Control Laboratory of INB/URA, using Liquid Scintillation Spectrometry and ICP-MS. The results were analyzed using linear regression (R2) and spatial interpolation (IDW). A strong correlation was observed between U-nat, 210Pb, and 226Ra (R2 > 0.68), indicating that their mobility depends on the mineralogical composition and soil pH. The highest concentrations occurred in clayey soils rich in iron oxides, particularly in the western portion of the study area. The radiogenic behavior of the isotopes suggests the influence of weathering and adsorption processes on the dispersion of radioelements. It is concluded that the Lagoa Real Complex presents significant geochemical anomalies, requiring continuous monitoring and public policies aimed at mitigating radiological and chemical risks in the region.

Accessibility is crucial to ensure that all users have interfaces that meet their needs and preferences, promoting inclusion in all sociocultural spaces. In the context of healthcare services in Guanambi-BA, deaf individuals face communication difficulties due to the lack of accessibility in identification signs, which are written only in Portuguese, excluding those who use Brazilian Sign Language (Libras) as their first language. This creates barriers that compromise the autonomy and quality of life of these individuals. Faced with this reality, the objective of this work was to produce images and videos in Libras, accessible through QR codes inserted in the identification signs of healthcare sectors. This initiative aims to promote the autonomy and dignity of deaf individuals using healthcare services in Guanambi, Bahia, contributing to a more inclusive and egalitarian society.